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Soft Computing for Image Processing

  • Book
  • © 2000

Overview

Editors:
  1. Sankar K. Pal

    1. Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

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  2. Ashish Ghosh

    1. Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

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  3. Malay K. Kundu

    1. Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

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  • Application oriented comprehensive volume
  • Practical, timely, effective, comprehensive, understandable and informative, along with an introduction to the subject

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 42)

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Table of contents (21 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Soft Computing and Image Analysis: Features, Relevance and Hybridization

    1. Soft Computing and Image Analysis: Features, Relevance and Hybridization

      • Sankar K. Pal, Ashish Ghosh, Malay K. Kundu
      Pages 1-20

  3. Preprocessing and Feature Extraction

    1. Front Matter

      Pages 21-21
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    2. Image Filtering Using Evolutionary Neural Fuzzy Systems

      • Fabrizio Russo
      Pages 23-43

    3. Edge Extraction Using Fuzzy Reasoning

      • Todd Law, Koji Yamada, Daisuke Shibata, Tsuyoshi Nakamura, Lifeng He, Hidenori Itoh
      Pages 44-78

    4. Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithm

      • Suman K. Mitra, C. A. Murthy, M. K. Kundu
      Pages 79-100

    5. Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images

      • Sunanda Mitra, Ramiro Castellanos, Su-Yu Yang, Surya Pemmaraju
      Pages 101-129

    6. On Fuzzy Thresholding of Remotely Sensed Images

      • B. Uma Shankar, A. Ghosh, S. K. Pal
      Pages 130-161

    7. Image Compression Using Pixel Neural Networks

      • Wladyslaw Skarbek
      Pages 162-182

    8. Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches

      • Lifeng He, Yuyan Chao, Tsuyoshi Nakamura, Hidenori Itoh
      Pages 183-204

    9. Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization

      • Nicolaos B. Karayiannis, Ted C. Wang
      Pages 205-245

    10. Soft Computing and Image Analysis

      • Vito Di Gesu
      Pages 246-259

    11. Fuzzy Interpretation of Image Data

      • Joon H. Han, Tae Y. Kim, László T. Kóczy
      Pages 260-295

  4. Classification

    1. Front Matter

      Pages 297-297
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    2. New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding

      • Michel Grabisch
      Pages 299-317

    3. Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognition

      • Nikola K. Kasabov, Brendon J. Woodford, Steven I. Israel
      Pages 318-336

    4. Neuro-Fuzzy Computing: Structure, Performance Measure and Applications

      • P. A. Stadterl, N. K. Bose
      Pages 337-374

    5. Knowledge Reuse Mechanisms for Categorizing Related Image Sets

      • Kurt D. Bollacker, Joydeep Ghosh
      Pages 375-400

    6. Symbolic Data Analysis for Image Processing

      • K. Chidananda Gowda, H. N. Srikanta Prakash, P. Nagabhushan
      Pages 401-428

  5. Applications

    1. Front Matter

      Pages 429-429
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Keywords

About this book

Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc.

Editors and Affiliations

  • Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

    Sankar K. Pal, Ashish Ghosh, Malay K. Kundu

Bibliographic Information

  • Book Title: Soft Computing for Image Processing

  • Editors: Sankar K. Pal, Ashish Ghosh, Malay K. Kundu

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1858-1

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2000

  • Hardcover ISBN: 978-3-7908-1268-8Published: 17 February 2000

  • Softcover ISBN: 978-3-7908-2468-1Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1858-1Published: 19 March 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XVII, 591

  • Number of Illustrations: 332 b/w illustrations

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, IT in Business

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